Dynamics-Guided Diffusion Model for Sensor-less Robot Manipulator Design

2024-02-23

Conference on Robot Learning

AI Summary

This work addresses blind manipulation tasks for sensorless robotic end-effectors. Given an object’s geometry and target pose, we propose a task-agnostic mechanical structure synthesis method that requires no task-specific training. Our approach models the manipulation task as an “interaction contour,” explicitly decoupling task specification from dynamics awareness. We introduce a novel dynamics-gradient-guided diffusion generation paradigm, integrating a geometric diffusion model, a dynamics network pretrained without task supervision, and open-loop motion planning. Compared to optimization-based and unguided diffusion baselines, our method achieves average task success rate improvements of 31.5% and 45.3%, respectively, with only 0.8 seconds per design generation. The core contribution is the first zero-shot generative framework that decouples task specification from dynamics modeling, enabling high-precision blind manipulation under open-loop, parallel-motion execution.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We present Dynamics-Guided Diffusion Model (DGDM), a data-driven framework for generating task-specific manipulator designs without task-specific training. Given object shapes and task specifications, DGDM generates sensor-less manipulator designs that can blindly manipulate objects towards desired motions and poses using an open-loop parallel motion. This framework 1) flexibly represents manipulation tasks as interaction profiles, 2) represents the design space using a geometric diffusion model, and 3) efficiently searches this design space using the gradients provided by a dynamics network trained without any task information. We evaluate DGDM on various manipulation tasks ranging from shifting/rotating objects to converging objects to a specific pose. Our generated designs outperform optimization-based and unguided diffusion baselines relatively by 31.5% and 45.3% on average success rate. With the ability to generate a new design within 0.8s, DGDM facilitates rapid design iteration and enhances the adoption of data-driven approaches for robot mechanism design. Qualitative results are best viewed on our project website https://dgdm-robot.github.io/.

Problem

• Generating task-specific manipulator designs without task-specific training
• Creating sensor-less manipulator designs for blind object manipulation
• Efficiently searching design space using dynamics-guided diffusion model

Innovation

• Dynamics-Guided Diffusion Model for manipulator design
• Task-specific designs without task-specific training
• Efficient design space search using dynamics network